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About This Role
*To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.*
Job Category
Product
Job Details
About Salesforce
Salesforce is the \#1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level\-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
At Salesforce, we are pioneering the Agentic Enterprise. MuleSoft has long been the world's leading integration and API management platform, connecting the world's data. Now, as enterprises rapidly deploy autonomous AI agents, they face a new challenge: unmanaged agent sprawl across disparate platforms and clouds.
To solve this, MuleSoft has introduced Agent Fabric —the unified AI control plane for the agentic era. We are providing the foundational infrastructure to discover, orchestrate, govern, and observe multi\-vendor AI agents securely across the modern enterprise.
We are seeking a visionary Senior Vice President of Product Management to lead the strategy, development, and execution of MuleSoft Agent Fabric.
In this executive role, you will act as the business and product owner for our AI Control Plane. You will be responsible for building the central nervous system that acts as the "air traffic controller" for the AI enterprise. You will lead a world\-class team of product managers to deliver a platform where customers can seamlessly govern Salesforce Agentforce agents alongside third\-party agents (like Amazon Bedrock, Google Vertex AI, and Microsoft Copilot).
Your portfolio will sit at the bleeding edge of AI and integration, heavily leveraging the Model Context Protocol (MCP) to bridge traditional API gateways with dynamic LLM routing, runtime traffic enforcement, and enterprise knowledge stores.
What You Will Do
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- Define the AI Control Plane Vision: Own the end\-to\-end product strategy for MuleSoft Agent Fabric, defining how enterprises will manage, deploy, and secure agentic workflows at scale.
- Drive the Core Capabilities: Lead the product development across the three critical pillars of the AI Control Plane:
+ Governance \& Security: Build the central nervous system for AI trust. Develop enterprise\-grade guardrails, real\-time threat detection (e.g., prompt injection, data exfiltration), automated PII/PHI redaction, and strict Role\-Based Access Control (RBAC) to ensure all agentic traffic complies with corporate policies and regulatory frameworks.
+ AI FinOps: Bring financial accountability to generative AI. Deliver capabilities that allow enterprises to track token usage, dynamically route LLM requests to optimize for cost vs. performance, attribute AI spend to specific departments, and establish budget thresholds to eliminate bill shock.
+ Agent Operations: Create the ultimate command center for the agentic lifecycle. Define the roadmap for deploying, versioning, and monitoring multi\-agent systems. Build deep observability features—tracing agent reasoning, context windows, and multi\-vendor handoffs—to ensure autonomous systems run reliably and transparently at scale.
+ Lead a World\-Class Team: Hire, mentor, and scale a global team of high\-performing Directors and Group Product Managers.
+ Cross\-Cloud Alignment: Partner closely with the Salesforce Agentforce, Einstein, and Data Cloud leadership teams to ensure seamless native integration and cohesive GTM motions.
+ Ecosystem \& Standards: Champion open standards (like MCP) and build strategic partnerships with major LLM providers, AI FinOps tooling, and security vendors.
+ Evangelize \& Launch: Serve as the face of MuleSoft's AI infrastructure at major industry events (Dreamforce, TrailblazerDX), to industry analysts (Gartner, Forrester), and directly to C\-level customers.
What You Need to Succeed
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- Experience: 15\+ years of progressive product management experience in enterprise software, with at least 5\+ years managing large product organizations at the VP level or above.
- Domain Expertise: Deep technical understanding of API Management, Enterprise Integration (iPaaS), AI infrastructure, LLM routing, and API security.
- AI Architecture Fluent: Highly conversant in modern AI paradigms including Agentic architectures, Retrieval\-Augmented Generation (RAG), Model Context Protocol (MCP), and vector databases.
- Execution Focus: Proven track record of taking complex, v1 enterprise infrastructure products from zero\-to\-one, and scaling them to hundreds of millions in ARR.
- Customer Obsession: Ability to engage directly with CIOs, CISOs, and Chief AI Officers to distill their governance, cost, and operational anxieties into elegant, scalable product solutions.
- Exceptional Communicator: Elite written and verbal communication skills, with the ability to simplify highly complex distributed networking and AI concepts into compelling market narratives.
Preferred Qualifications
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- Previous experience building API Gateways, Service Meshes, or Developer Platforms.
- Familiarity with the Salesforce ecosystem, MuleSoft Anypoint Platform, and Data Cloud architecture.
- An advanced degree in Computer Science, Engineering, or a related technical field.
Unleash Your Potential
When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and *be your best* , and our AI agents accelerate your impact so you can *do your best* . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Accommodations
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Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non\-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job\-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.\&\#xa;\&\#xa;The typical base salary range for this position is $380,000 \- $418,000 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $380,000 \- $456,000 annually.\&\#xa;\&\#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
Salary Context
This $380K-$456K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Salesforce, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($418K) sits 95% above the category median. Disclosed range: $380K to $456K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Salesforce AI Hiring
Salesforce has 10 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Seattle, WA, US, San Francisco, CA, US, Bellevue, WA, US. Compensation range: $194K - $456K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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